import numpy as np
import cv2
from matplotlib import pyplot as plt

img = cv2.imread('../../../../../large_data/pic/football.jpg', 0)
dft = cv2.dft(np.float32(img), flags=cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)
print(f'dft_shift.shape: {dft_shift.shape}')
print(f'dft_shift[:, :, 0]: dtype: {dft_shift[:, :, 0].dtype}, shape: {dft_shift[:, :, 0].shape}, min: {dft_shift[:, :, 0].min()}, max: {dft_shift[:, :, 0].max()}')
print(f'dft_shift[:, :, 1]: dtype: {dft_shift[:, :, 1].dtype}, shape: {dft_shift[:, :, 1].shape}, min: {dft_shift[:, :, 1].min()}, max: {dft_shift[:, :, 1].max()}')
magnitude_spectrum = 20 * np.log(cv2.magnitude(dft_shift[:, :, 0], dft_shift[:, :, 1]))
print('magnitude_spectrum.shape', magnitude_spectrum.shape)
# magnitude_spectrum = 20*np.log(abs(dft_shift))  # RuntimeWarning: divide by zero encountered in log
plt.figure()
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(magnitude_spectrum, cmap='gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()

plt.figure()
rows, cols = img.shape
crow, ccol = rows // 2, cols // 2
# create a mask first, center square is 1, remaining all zeros
mask = np.zeros((rows, cols, 2), np.uint8)
mask[crow - 30:crow + 30, ccol - 30:ccol + 30] = 1
# apply mask and inverse DFT
fshift = dft_shift * mask
f_ishift = np.fft.ifftshift(fshift)
img_back = cv2.idft(f_ishift)
img_back = cv2.magnitude(img_back[:, :, 0], img_back[:, :, 1])
plt.subplot(121), plt.imshow(img, cmap='gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(img_back, cmap='gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()
